code: def _unpack_labels(self, enc_bboxes, enc_classes):
      # from keras/anchors.py
  
      enc_bboxes_layers = []
      enc_classes_layers = []
  
      if self._device == "gpu":
          enc_bboxes = enc_bboxes.gpu()
          enc_classes = enc_classes.gpu()
  
      count = 0
      for level in range(
          self._anchors.min_level, self._anchors.max_level + 1
      ):
          feat_size = self._anchors.feat_sizes[level]
          steps = (
              feat_size["height"]
              * feat_size["width"]
              * self._anchors.get_anchors_per_location()
          )
  
          enc_bboxes_layers.append(
              dali.fn.reshape(
                  enc_bboxes[count : count + steps, 0:4],
                  shape=[feat_size["height"], feat_size["width"], -1],
              )
          )
          enc_classes_layers.append(
              dali.fn.reshape(
                  enc_classes[count : count + steps],
                  shape=[feat_size["height"], feat_size["width"], -1],
              )
          )
  
          count += steps
  
      return enc_bboxes_layers, enc_classes_layers
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/pipeline/dali/efficientdet_pipeline.py
imports: import nvidia.dali
  from nvidia.dali import pipeline_def
  import nvidia.dali.plugin.tf
  import tensorflow
  import math
  from absl import logging
  from glob import glob
  from pipeline import anchors
  from utils import InputType
  from .None import ops_util
label_desc: Use image node_id 4 for calling the EfficientDetPipeline obj's underlying member method code with example usage: obj._unpack_labels(enc_bboxes, enc_classes) and returns: enc_bboxes_layers,  enc_classes_layers